It has long been evident that there are individual differences in risk of initiation of substance use, abuse, dependence, and relapse. Furthermore, there is heterogeneity in response to various aspects of any prevention and treatment program, both across individuals and within individuals across time. In an exciting trend in the drug abuse field, researchers have come to realize that it is possible to utilize this heterogeneity when designing prevention and treatment programs, thus greatly improving the effectiveness of these programs. This is done via individually tailored treatments. In individually tailored treatments the treatment level and type is readjusted at each time point according to the individual's present need. These treatment designs show considerable promise for helping to ameliorate our nation's drug abuse problem. Yet very little is known about how to design individually tailored treatments so as to maximize both their power and replicability. In order for these treatment designs to fulfill their potential, much scientific work on the design of the treatment is needed. Additionally once an individually tailored treatment is implemented, little is known about how to analyze the resulting data. My long-term career goal is to make an impact on our nation's drug problem by designing methods that can effectively utilize theory, clinical experience and experimental/observational data to inform the design and analysis of time varying treatments in substance abuse prevention and treatment. My present P-50 component addresses general evaluation issues posed by treatments that change with time (including the individually tailored treatment). The K-02 award would enable me to extend this research to the design of individually tailored treatments and to expand the methodology for evaluation of these treatments.
| Gunter, L; Zhu, J; Murphy, S A (2011) Variable Selection for Qualitative Interactions. Stat Methodol 1:42-55 |
| McGowan, Herle M; Nix, Robert L; Murphy, Susan A et al. (2010) Investigating the impact of selection bias in dose-response analyses of preventive interventions. Prev Sci 11:239-51 |
| Murphy, Susan A; Lynch, Kevin G; Oslin, David et al. (2007) Developing adaptive treatment strategies in substance abuse research. Drug Alcohol Depend 88 Suppl 2:S24-30 |
| Collins, Linda M; Murphy, Susan A; Strecher, Victor (2007) The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med 32:S112-8 |
| Murphy, Susan A; Oslin, David W; Rush, A John et al. (2007) Methodological challenges in constructing effective treatment sequences for chronic psychiatric disorders. Neuropsychopharmacology 32:257-62 |
| Pineau, Joelle; Bellemare, Marc G; Rush, A John et al. (2007) Constructing evidence-based treatment strategies using methods from computer science. Drug Alcohol Depend 88 Suppl 2:S52-60 |
| Murphy, Susan A; Collins, L M; Rush, A John (2007) Customizing treatment to the patient: adaptive treatment strategies. Drug Alcohol Depend 88 Suppl 2:S1-3 |
| Bray, Bethany Cara; Almirall, Daniel; Zimmerman, Rick S et al. (2006) Assessing the total effect of time-varying predictors in prevention research. Prev Sci 7:1-17 |
| Murphy, Susan A (2005) A Generalization Error for Q-Learning. J Mach Learn Res 6:1073-1097 |
| Collins, Linda M; Murphy, Susan A; Nair, Vijay N et al. (2005) A strategy for optimizing and evaluating behavioral interventions. Ann Behav Med 30:65-73 |
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